I always got the feeling people used Octave as "the next best thing" to having Matlab. Not everyone can shell out the money to MathWorks, especially outside academia, without having an employer pay for it. I used Matlab in college and found Octave to be mediocre. The speed leaves a great deal to be desired, especially if you've grown accustomed to the Parallel Toolbox and the inherent parallelization of the Statistics Toolbox since Matlab 2011a.

As for Python/R/Julia:
Python is more general purpose than Matlab/Octave, though I have seen a push for NumPy and SciPy.
R and Matlab work in somewhat different fields (exceptions to the rule: Functional data people in statistics seem to like Matlab). I always got the feeling the math/linear algebra/engineering people went with Matlab, while us statisticians went with R. There's plenty of information on Google about why that divide exists.
Julia is more direct comparison to Matlab, and the only reason I can offer why people would not use it versus Matlab is age and wealth of libraries. Julia's a very new language, relative to Matlab, so we may see a wider adoption in the coming years from the Julia project.